# Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders

arXiv: https://arxiv.org/abs/2004.05209 · HTML: https://mlsanity.com/paper/2004.05209/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series](https://mlsanity.com/paper/2505.20697/) (2025)
- [Detecting Batch Heterogeneity via Likelihood Clustering](https://mlsanity.com/paper/2601.09758/) (2026)
- [Generative Principal Component Regression via Variational Inference](https://mlsanity.com/paper/2409.02327/) (2024)
- [Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data](https://mlsanity.com/paper/2608.21551/) (2026)

## Abstract (arXiv)

Targeted stimulation of the brain has the potential to treat mental illnesses. We propose an approach to help design the stimulation protocol by identifying electrical dynamics across many brain regions that relate to illness states. We model multi-region electrical activity as a superposition of activity from latent networks, where the weights on the latent networks relate to an outcome of interest. In order to improve on drawbacks of latent factor modeling in this context, we focus on supervised autoencoders (SAEs), which can improve predictive performance while maintaining a generative model. We explain why SAEs yield improved predictions, describe the distributional assumptions under which SAEs are an appropriate modeling choice, and provide modeling constraints to ensure biological relevance of the learned network. We use the analysis strategy to find a network associated with stress that characterizes a genotype associated with bipolar disorder. This discovered network aligns with a previously used stimulation technique, providing experimental validation of our approach.